We are developing a new biomarker for fibromyalgia with multi-modal techniques, including  biochemical, brain-related, and physiological measures. Applying machine learning tools, we will attempt to classify fibromyalgia patients and their pain levels with neurophysiological and biochemical information. Further our model could be transformed into fully automated real-time detection of fibromyalgia and ongoing pain. The automated pain tracking has considerable potential to improve the efficacy of pain treatments, by providing just-in-time feedback and triggering interventions. Overall, the proposed project will contribute fundamental scientific knowledge about biochemical and neurophysiological signs of real-life pain and lay the groundwork for translational efforts to improve outcomes of pain self-management and reduce opioid use.This project is conducted in collaboration with Prof. Hossam Haick (Technion).